Referring expression generation has recently been the subject of the first Shared Task Challenge in NLG. In this paper, we analyse the systems that participated in the Challenge in terms of their algorithmic properties, comparing new techniques to classic ones, based on results from a new human task-performance experiment and from the intrinsic measures that were used in the Challenge. We also consider the relationship between different evaluation methods, showing that extrinsic taskperformance experiments and intrinsic evaluation methods yield results that are not significantly correlated. We argue that this highlights the importance of including extrinsic evaluation methods in comparative NLG evaluations
Shared Task Evaluation Challenges (stecs) have only recently begun in the field of nlg. The tuna ste...
Abstract. The premise of the work presented in this chapter is that much of the existing work on the...
The natural language generation litera-ture provides many algorithms for the generation of referring...
Referring expression generation has recently been the subject of the first Shared Task Challenge in ...
In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics tha...
In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics tha...
As one of the most well-defined subtasks in Natural Language Generation (NLG), the generation of ref...
The Natural Language Generation community is currently engaged in discussion as to whether and how t...
The Natural Language Generation community is currently engaged in discussion as to whether and how t...
We describe a corpus-based evaluation method- ology, applied to a number of classic algorithms in th...
One important subtask of Referring Expression Generation (REG) algorithms is to select the attribute...
The entry presented by the NIL research group of the Universidad Complutense de Madrid adapts existi...
the first shared-task evaluation challenge in the field of Natural Language Generation. Six teams su...
The natural language generation literature provides many algorithms for the generation of referring ...
Abstract Despite being the focus of intensive research, evaluation of algorithms that generate refer...
Shared Task Evaluation Challenges (stecs) have only recently begun in the field of nlg. The tuna ste...
Abstract. The premise of the work presented in this chapter is that much of the existing work on the...
The natural language generation litera-ture provides many algorithms for the generation of referring...
Referring expression generation has recently been the subject of the first Shared Task Challenge in ...
In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics tha...
In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics tha...
As one of the most well-defined subtasks in Natural Language Generation (NLG), the generation of ref...
The Natural Language Generation community is currently engaged in discussion as to whether and how t...
The Natural Language Generation community is currently engaged in discussion as to whether and how t...
We describe a corpus-based evaluation method- ology, applied to a number of classic algorithms in th...
One important subtask of Referring Expression Generation (REG) algorithms is to select the attribute...
The entry presented by the NIL research group of the Universidad Complutense de Madrid adapts existi...
the first shared-task evaluation challenge in the field of Natural Language Generation. Six teams su...
The natural language generation literature provides many algorithms for the generation of referring ...
Abstract Despite being the focus of intensive research, evaluation of algorithms that generate refer...
Shared Task Evaluation Challenges (stecs) have only recently begun in the field of nlg. The tuna ste...
Abstract. The premise of the work presented in this chapter is that much of the existing work on the...
The natural language generation litera-ture provides many algorithms for the generation of referring...